7 Mathematical skills
- Syllabus
- 2024
- Section
- 7
- Level
- —

A decimal records place value on both sides of the decimal point. Keep digits aligned by place value during addition or subtraction, and preserve units before carrying out a biological calculation.
| Move | Example |
|---|---|
| identify place value | in 0.025, 2 is hundredths and 5 is thousandths |
| align or convert units | 90 cm = 0.90 m before combining it with a speed in m s⁻¹ |
| calculate with the full values | 0.25 m ÷ 0.005 s = 50 m s⁻¹ |
| check position of the decimal point | dividing by a number smaller than 1 should increase the numerical value |
| report meaning and unit | write 50 m s⁻¹, not an unlabelled 50 |
A zero before the decimal point makes values below one unambiguous: write 0.5, not .5. Trailing zeros may communicate recorded precision—9.0 mg is expressed more precisely than 9 mg—even though the numerical value is equal.
Do not align numbers by their final digit or remove zeros before deciding what they communicate. A correct decimal with mismatched units is still a wrong biological calculation.
Standard form writes a non-zero number as a×10n, where 1≤∣a∣<10 and n is an integer. The coefficient shows the significant digits; the power of ten shows scale.
| Operation | Rule | Example |
|---|---|---|
| decimal to standard form | move the point until the coefficient is between 1 and 10; count places | 480000000=4.8×108 |
| standard to decimal | positive power moves right; negative power moves left | 2.0×10−1=0.20 |
| multiply | multiply coefficients and add powers | (5.0×109)(5.0×103)=25×1012=2.5×1013 |
| divide | divide coefficients and subtract powers | (6.0×108)/(3.0×102)=2.0×106 |
| add or subtract | first express terms with the same power of ten | 3.2×106+0.5×106=3.7×106 |
After calculating, renormalise the coefficient. 25×1012 has the right value but is not standard form because 25 is not between 1 and 10.
Ratios, fractions and percentages express one quantity relative to another. The denominator or comparison base must match the biological question before any arithmetic is performed.
| Need | Relationship | Worked biological example |
|---|---|---|
| simplify a ratio | divide every part by the same factor | 21 yellow : 10 brown = 2.1 : 1 |
| find a fraction of a total | fraction × total | half of 65 million = 0.5 × 65 million = 32.5 million |
| find a percentage of an amount | percentage ÷ 100 × amount | 16.7% of 28.0 g = 0.167 × 28.0 = 4.68 g |
| express a part as a percentage | part ÷ whole × 100 | 56 farmed out of 146 total = 38.4% |
| percentage change | change ÷ original × 100 | from 0.70 to 10.0 million: 9.30 ÷ 0.70 × 100 = 1330% approximately |
| powers and roots | a power repeats multiplication; a root reverses that power | 32=9 and 9=3 |
For percentage change, divide by the original value, not the final value. A percentage may exceed 100% when the increase is greater than the original. Keep ratios in the requested order and attach units to calculated amounts.
An estimate replaces values with nearby easy numbers to find an approximate result without a calculator. Its purpose is to predict the scale and expose implausible calculator entries, not to replace a required accurate answer.
| Move | Example using (4.8×108)(0.0040) |
|---|---|
| round to one useful significant figure | 4.8×108≈5×108 and 0.0040=4×10−3 |
| calculate mentally | (5×4)×108−3=20×105 |
| write the scale clearly | 20×105=2×106 |
| compare with the accurate result | 1.92×106 is close to 2×106, so its order of magnitude is plausible |
Round enough to make the arithmetic simple but keep the important scale. If one value is just above and another just below a convenient number, note whether both rounding choices push the estimate in the same direction.
An estimate should not contain more apparent precision than the original calculation. A result of 2.000×106 is not a sensible estimate here; write about 2×106 and retain the approximation sign or wording.
Significant figures count meaningful digits from the first non-zero digit. A final biological result should not imply finer precision than the measurements used to calculate it.
| Value | Significant figures and reason |
|---|---|
| 0.00450 | 3; leading zeros locate the decimal point, while the final zero is significant |
| 1200 | ambiguous without context; 1.2×103 states 2 significant figures |
| 12.46 to 3 s.f. | 12.5 because the next digit is 6, so 4 rounds up |
| 0.06784 to 2 s.f. | 0.068 because counting starts at 6 and the next digit rounds 7 up |
Keep extra digits during intermediate steps and round once at the end. Match the requested significant figures and retain the unit.
Decimal places count positions after the decimal point; significant figures count meaningful digits. For example, 0.0125 has four decimal places but three significant figures.
The arithmetic mean shares the total of all included observations equally across their number. It summarises the centre of repeated numerical data but does not show their spread.
mean=sumofincludedvalues/numberofincludedvalues
For gelatine volumes 0.55, 0.54 and 0.61 cm³, the mean is (0.55+0.54+0.61)/3=0.5666… cm³, reported as 0.57 cm³ to two decimal places.
Count only the values actually included. Do not discard an anomalous result merely because it changes the mean; exclusion needs a recorded evidence-based reason, and the reported mean should make that decision clear.
A bar chart compares numerical values for distinct categories. Because the categories are separate rather than continuous, the bars are separated by gaps.
| Construct | Interpret |
|---|---|
| place categories on the horizontal axis | identify the named group represented by each bar |
| label the vertical axis with variable and unit | read values from the scale, not from apparent bar area |
| start at zero unless a clearly shown break is justified | compare heights using the same scale |
| use equal bar widths and gaps | state the largest, smallest or difference with values |
If unlikely and likely groups have LH concentrations of 5 and 45 arbitrary units, the likely category is 40 units higher and nine times the unlikely value.
Do not join category bars into a continuous shape. A histogram looks similar but represents continuous grouped intervals, so its bars touch and may use frequency density.
A frequency table records how many observations fall in each value or class interval. A histogram displays grouped continuous data with touching bars whose areas represent frequencies.
| Data move | Rule |
|---|---|
| define classes | make intervals non-overlapping and cover every possible value |
| tally observations | place each observation in exactly one class |
| record frequency | count the tally in each class and check the total equals the sample size |
| draw equal-width histogram classes | bar height may be frequency because equal widths preserve area comparisons |
| draw unequal-width classes | use frequency density = frequency ÷ class width, so bar area equals frequency |
Bar height alone does not represent frequency when class widths differ. Histograms are for continuous grouped measurements; ordinary bar charts are for separate categories.
Sampling measures a manageable subset to infer properties of a larger population. The sample must be selected without systematic bias and be large and repeated enough to capture natural variation.
| Situation | Defensible sampling design |
|---|---|
| organisms across an area | overlay a grid, choose coordinates randomly, use equal-sized quadrats and count consistently |
| change along a gradient | place quadrats at fixed intervals along a transect |
| mobile organisms | use a defined trapping method for equal times and avoid counting the same individual twice where possible |
| microscopic density | count in known equal areas, repeat across randomly selected fields of view, find mean density and scale to total area |
Two stomata in 0.4 mm×0.4 mm=0.0016 cm² give a density of 2/0.0016=1250 stomata cm⁻². Applied to 150 cm², the estimate is 187500 stomata.
A large convenient sample can still be biased. More repeats improve representation only when locations, times or individuals are selected by a method appropriate to the population and question.
Probability measures how likely an outcome is, from 0 for impossible to 1 for certain. For equally likely outcomes, it is the number of favourable outcomes divided by the total number of possible outcomes.
P(event)=favourableoutcomes/totalpossibleoutcomes
For a heterozygous monohybrid cross Aa×Aa, one of four equally likely genotype outcomes is aa, so P(aa)=1/4=0.25=25%. For two independent events, multiply their probabilities.
A probability predicts a long-run proportion, not an exact result in a small family or sample. Observed frequencies may differ by chance, and events must be independent before their probabilities are multiplied.
The median is the middle value after numerical data are ordered; the mode is the value or category that occurs most often. They answer different questions about a dataset.
| Measure | Method | Useful when |
|---|---|---|
| median | order values; choose the middle, or average the two middle values when the count is even | extreme values would pull the mean away from a typical central value |
| mode | count occurrences and select the most frequent value or category | the most common outcome matters, including non-numerical categories |
For 6, 8, 8, 9, 16, the median is 8 and the mode is 8. For 6, 8, 9, 16, the median is (8+9)/2=8.5 and there is no mode because no value repeats.
The median cannot be found from unordered positions, and a dataset may have no mode or more than one mode. Do not call the largest value the mode unless it is also most frequent.
A scatter diagram plots paired measurements for two numerical variables. The overall point pattern can show positive correlation, negative correlation or no clear correlation.
| Pattern | Interpretation |
|---|---|
| points rise from left to right | positive correlation: larger values of one variable tend to accompany larger values of the other |
| points fall from left to right | negative correlation |
| points show no direction | no clear correlation in the observed range |
| points lie close to a trend | stronger correlation than a widely scattered pattern |
| one point lies far from the pattern | possible anomaly; check it without deleting it automatically |
Plot the independent variable on the horizontal axis and its paired dependent value vertically. A best-fit line or curve follows the overall pattern rather than joining each point.
Correlation alone does not prove that one variable causes the other. A third variable, reverse causation or chance may explain the pattern, and extrapolation beyond the observed range is uncertain.
An order of magnitude is a factor of ten. Expressing a quantity in standard form reveals its scale and allows rapid comparisons between very large or very small biological values.
| Move | Example |
|---|---|
| express each value in standard form | a bacterium 2×10−6 m; a cell 2×10−5 m |
| compare powers of ten | exponents differ by −5−(−6)=1 |
| convert exponent difference to a factor | 101=10, so the cell is about ten times longer |
| estimate a product or quotient | combine rounded coefficients and add or subtract exponents |
A difference of two orders of magnitude means a factor of 102=100, not a difference of 2. Coefficients near a power boundary can affect the nearest order, so keep them visible until the comparison is justified.
Changing the subject rewrites an equation so the required symbol stands alone while the relationship remains equivalent. Apply inverse operations to both sides, preserving brackets and powers.
| Starting relationship | Required subject | Equivalent form |
|---|---|---|
| c=a/v | v | multiply by v, then divide by c: v=a/c |
| r=d/t | t | t=d/r |
| B=m/h2 | m | m=Bh2 |
| B=m/h2 | h | h=m/B for a positive physical height |
Urine concentration is c=a/v. With amount a=600 milliosmoles and maximum concentration c=1400 milliosmoles dm⁻³, first rearrange to v=a/c, then calculate v=600/1400=0.429 dm³, or about 429 cm³.
Moving a term across an equals sign is shorthand for applying an inverse operation to both sides. Do not change a sign or invert a quantity without showing the operation that keeps the equation balanced.
Substitution replaces each symbol with its numerical value. Before calculating, convert quantities to the units required by the equation and use brackets so powers and denominators apply to the intended value.
BMI=massinkg/(heightinm)2
| Move | BMI example |
|---|---|
| identify symbols and required units | mass = 60 kg; height must be in metres |
| convert units | 165 cm = 1.65 m |
| substitute with brackets | BMI=60/(1.65)2 |
| calculate in operation order | (1.65)2=2.7225, then 60/2.7225=22.0 |
| interpret only after calculation | 22.0 lies in the stated healthy category |
Do not square only part of a substituted height or mix centimetres with a formula defined in metres. Units are part of the input, not decoration added after arithmetic.
Solving an equation finds the numerical value of an unknown that makes both sides equal. Undo operations in reverse order and perform the same operation on both sides.
| Equation | Balanced steps | Solution check |
|---|---|---|
| 3x+6=24 | subtract 6: 3x=18; divide by 3: x=6 | 3(6)+6=24 |
| y/5−2=4 | add 2: y/5=6; multiply by 5: y=30 | 30/5−2=4 |
| 2z2=50 | divide by 2: z2=25; take the relevant root | for a positive biological length, z=5 |
When an equation contains measured quantities, retain units through the solution and judge whether negative or alternative roots make physical sense in that context.
Changing the subject produces a formula in symbols; solving produces a value after known quantities are supplied. Always substitute the result back into the original equation to detect an arithmetic or sign error.
Translating between graphical and numerical form preserves the variables, units and scale while changing how the relationship is represented. Read coordinates from the axes before describing a pattern.
| Graph-to-number move | Number-to-graph move |
|---|---|
| identify the horizontal variable, vertical variable and units | put each paired observation into an (x,y) row |
| locate a stated x value and move vertically to the plotted point or line | choose an axis range and regular scale covering all values |
| move horizontally to read the corresponding y value | plot each pair at its coordinate |
| calculate a difference or rate from selected coordinates when required | use a key when several datasets share the axes |
| describe direction, range, plateau or fluctuation with values | retain the exact table values even when the graph summarises their pattern |
If a myelinated neurone graph gives 4.4 m s⁻¹ at a diameter of 1.0 µm, the numerical translation is the coordinate (1.0μm,4.4ms−1). State both values and units.
Reading between plotted values is interpolation; reading beyond the measured range is extrapolation and is less secure. Do not infer a value from visual height without checking the scale increments.
A two-variable graph places the independent variable on the horizontal axis and the dependent variable on the vertical axis so every paired observation has one unambiguous coordinate.
| Plotting decision | Required feature |
|---|---|
| choose axes | independent variable on x; dependent variable on y |
| label completely | variable name and unit on each axis |
| set scales | linear, regular increments that use much of the available grid and include all values |
| plot points | small, accurate marks at every coordinate |
| represent connection | join ordered continuous measurements only when instructed or scientifically justified; otherwise use a suitable best-fit line or leave points unjoined |
| compare datasets | use distinguishable lines or symbols and a complete key; do not extrapolate beyond the data |
For seedling dry mass measured on days 4, 8, 12, 16 and 20, plot day on x and dry mass in g on y. Plot fertiliser and no-fertiliser values on the same scales and label both series so their changes can be compared directly.
Discrete categories do not become continuous merely because points can be joined. A line between time points shows an ordered change; a bar chart is usually clearer for unrelated categories.
For a linear graph, slope measures the change in y per unit change in x, and the vertical intercept is the value of y where the line crosses the y-axis at x=0.
slopem=changeiny/changeinx=(y2−y1)/(x2−x1);linearformy=mx+c
| Move | Example for y=0.8x+0.2 |
|---|---|
| select two well-separated points on the straight line | (1,1.0) and (6,5.0) |
| calculate vertical and horizontal changes | Δy=4.0 and Δx=5 |
| divide and attach compound units | m=4.0/5=0.8 units of y per unit of x |
| read or calculate the intercept | at x=0, y=c=0.2 |
| interpret | each one-unit rise in x is associated with a 0.8-unit rise in y |
Use points on the linear line, not necessarily two noisy raw points. The intercept may have no biological meaning when x=0 is impossible or lies outside the measured range; report it mathematically without inventing a biological claim.
Length is one-dimensional, area measures a two-dimensional surface, and volume measures three-dimensional space. Convert all lengths to the same unit before applying a formula, then square or cube that unit in the result.
| Shape or quantity | Formula | Example |
|---|---|---|
| rectangle area | A=lw | 8 cm×3 cm=24 cm2 |
| triangle area | A=21bh using perpendicular height | 21(6 cm)(4 cm)=12 cm2 |
| cube surface area | S=6s2 because a cube has six square faces | for s=4μm, S=6(42)=96μm2 |
| cube volume | V=s3 | for s=4μm, V=43=64μm3 |
Label the required dimension, select the matching formula, substitute one consistent length unit, calculate, and check that the final unit is squared for area or cubed for volume. Surface-area-to-volume comparisons must use compatible units before division.
The sloping side of a triangle is not its height unless it is perpendicular to the chosen base. For a cube, s2 is the area of one face; total surface area is 6s2, while volume is s3.